import os
import random
import time
import argparse
import numpy as np
from models.gradcam import YOLOV5GradCAM, YOLOV5GradCAMPP
from models.yolo_v5_object_detector import YOLOV5TorchObjectDetector
import cv2

# names = ['trashcan', 'slippers', 'wire', 'socks', 'carpet', 'book', 'feces', 'curtain', 'stool', 'bed',
#          'sofa', 'close stool', 'table', 'cabinet']
names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
         'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
         'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
         'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
         'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
         'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
         'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
         'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
         'hair drier', 'toothbrush']  # class names
# yolov5s网络中的三个detect层
target_layers = ['model_17_cv3_act', 'model_20_cv3_act', 'model_23_cv3_act']
# Arguments
parser = argparse.ArgumentParser()
parser.add_argument('--model-path', type=str, default="weights/yolov5s.pt", help='Path to the model')
parser.add_argument('--img-path', type=str, default='data/images', help='input image path')
parser.add_argument('--output-dir', type=str, default='outputs/', help='output dir')
parser.add_argument('--img-size', type=int, default=640, help="input image size")
parser.add_argument('--target-layer', type=str, default='model_17_cv3_act',
                    help='The layer hierarchical address to which gradcam will applied,'
                         ' the names should be separated by underline')
parser.add_argument('--method', type=str, default='gradcam', help='gradcam method')
parser.add_argument('--device', type=str, default='cpu', help='cuda or cpu')
parser.add_argument('--no_text_box', action='store_true',
                    help='do not show label and box on the heatmap')
args = parser.parse_args()


def get_res_img(bbox, mask, res_img):
    mask = mask.squeeze(0).mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).detach().cpu().numpy().astype(
        np.uint8)
    heatmap = cv2.applyColorMap(mask, cv2.COLORMAP_JET)
    # n_heatmat = (Box.fill_outer_box(heatmap, bbox) / 255).astype(np.float32)
    n_heatmat = (heatmap / 255).astype(np.float32)
    res_img = res_img / 255
    res_img = cv2.add(res_img, n_heatmat)
    res_img = (res_img / res_img.max())
    return res_img, n_heatmat


def plot_one_box(x, img, color=None, label=None, line_thickness=3):
    # this is a bug in cv2. It does not put box on a converted image from torch unless it's buffered and read again!
    cv2.imwrite('temp.jpg', (img * 255).astype(np.uint8))
    img = cv2.imread('temp.jpg')

    # Plots one bounding box on image img
    tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
    color = color or [random.randint(0, 255) for _ in range(3)]
    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
    cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
    if label:
        tf = max(tl - 1, 1)  # font thickness
        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
        outside = c1[1] - t_size[1] - 3 >= 0  # label fits outside box up
        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 if outside else c1[1] + t_size[1] + 3
        outsize_right = c2[0] - img.shape[:2][1] > 0  # label fits outside box right
        c1 = c1[0] - (c2[0] - img.shape[:2][1]) if outsize_right else c1[0], c1[1]
        c2 = c2[0] - (c2[0] - img.shape[:2][1]) if outsize_right else c2[0], c2[1]
        cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled
        cv2.putText(img, label, (c1[0], c1[1] - 2 if outside else c2[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf,
                    lineType=cv2.LINE_AA)
    return img


# 检测单个图片
def main(img_path):
    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
    device = args.device
    input_size = (args.img_size, args.img_size)
    # 读入图片
    img = cv2.imread(img_path)  # 读取图像格式：BGR
    print('[INFO] Loading the model')
    # 实例化YOLOv5模型，得到检测结果
    model = YOLOV5TorchObjectDetector(args.model_path, device, img_size=input_size, names=names)
    # img[..., ::-1]: BGR --> RGB
    # (480, 640, 3) --> (1, 3, 480, 640)
    torch_img = model.preprocessing(img[..., ::-1])
    tic = time.time()
    # 遍历三层检测层
    for target_layer in target_layers:
        # 获取grad-cam方法
        if args.method == 'gradcam':
            saliency_method = YOLOV5GradCAM(model=model, layer_name=target_layer, img_size=input_size)
        elif args.method == 'gradcampp':
            saliency_method = YOLOV5GradCAMPP(model=model, layer_name=target_layer, img_size=input_size)
        masks, logits, [boxes, _, class_names, conf] = saliency_method(torch_img)  # 得到预测结果
        result = torch_img.squeeze(0).mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).detach().cpu().numpy()
        result = result[..., ::-1]  # convert to bgr
        # 保存设置
        imgae_name = os.path.basename(img_path)  # 获取图片名
        save_path = f'{args.output_dir}{imgae_name[:-4]}/{args.method}'
        if not os.path.exists(save_path):
            os.makedirs(save_path)
        print(f'[INFO] Saving the final image at {save_path}')
        # 遍历每张图片中的每个目标
        for i, mask in enumerate(masks):
            # 遍历图片中的每个目标
            res_img = result.copy()
            # 获取目标的位置和类别信息
            bbox, cls_name = boxes[0][i], class_names[0][i]
            label = f'{cls_name} {conf[0][i]}'  # 类别+置信分数
            # 获取目标的热力图
            res_img, heat_map = get_res_img(bbox, mask, res_img)
            res_img = plot_one_box(bbox, res_img, label=label, color=colors[int(names.index(cls_name))],
                                   line_thickness=3)
            # 缩放到原图片大小
            res_img = cv2.resize(res_img, dsize=(img.shape[:-1][::-1]))
            output_path = f'{save_path}/{target_layer[6:8]}_{i}.jpg'
            cv2.imwrite(output_path, res_img)
            print(f'{target_layer[6:8]}_{i}.jpg done!!')
    print(f'Total time : {round(time.time() - tic, 4)} s')


if __name__ == '__main__':
    # 图片路径为文件夹
    if os.path.isdir(args.img_path):
        img_list = os.listdir(args.img_path)
        print(img_list)
        for item in img_list:
            # 依次获取文件夹中的图片名，组合成图片的路径
            main(os.path.join(args.img_path, item))
    # 单个图片
    else:
        main(args.img_path)
